Author: Yau, Kim-ming David
Title: Short-term load forecast for cold-thermal storage application
Degree: Eng.D.
Year: 2007
Subject: Hong Kong Polytechnic University -- Dissertations.
Electric power-plants -- Load -- Forecasting.
Electric power consumption -- Forecasting.
Neural computers.
Cold storage -- Energy conservation.
Department: Department of Electrical Engineering
Pages: v, 152 p. : col. ill. ; 30 cm.
Language: English
Abstract: Growth of electricity consumption is the unique process with the economic growth and GDP growth in a country or city. However, how to maximize the utilization of the generation, transmission and distribution capacity is the key objective of the electricity supplier. That is the reason why the electricity tariff structure is inherited with the "Demand Side Management" ingredient. In order to maximize the utilization of the system capacity, flattening the system load profile is essential for the electricity supplier and the customer. Most of the electricity tariff structure consists of incentive to encourage the customer to use electricity in Off-peak period rather than in On-peak period. If there is electricity supply shortage in the city, penalty for using more electricity in On-peak period could also be applied or the electricity supply company applies a very high Maximum Demand Charge to the customer if the customer's usage exceeds their contracted demand. Because of this reason, large usage customer always makes effort to shift their consumption from On-peak hours to Off-peak hours. However, most of the customers cannot shift their operations due to the nature of their business. One of the feasible ways is to store some energy in the Off-peak period and use it in the On-peak period so as to reduce the consumption in the On-peak hours. That's why Cold-thermal Storage application is common in some countries and cities. However, there are still rooms to improve the operational effectiveness of the Cold-thermal Storage Plant if we could know the cooling load demand of next day. Moreover, there is not much research addressing the overall impact of the operations of the Cold-thermal Storage to the electricity cost of the customer nor any research specifically focus on the inter-relationship between, tariff structure, load pattern of customer, characteristic of the Cold-thermal Storage plant, operations of the Cold-thermal Storage Plant and the load forecast mechanism. This thesis aims at examining the above relationship especially in the application of Neural Network of load forecast for the operations of the Cold-thermal Storage Plant. The thesis will compare the performance of the most common Neural Network algorithm in load forecast and select the most appropriate one. The three kinds of Neural Network Algorithm are Elman Recurrent Network, Feedforward Neural Network and Least Mean Square Algorithm. The selected Neural Network algorithm will also be used for the load forecast for the operations of the Cold-thermal Storage Plant. The next stage is to use the forecast data to the simulation model of the Cold-thermal Storage Plant operation. This sophisticated simulation model is specially designed for the operations of the Cold-thermal Storage Plant and it can optimize the operating pattern of the Cold-thermal storage Plant for achieving savings in overall electricity cost. Lastly, the author also points out the potential of further research in this field in order to commercialize this concept for industrial application.
Rights: All rights reserved
Access: restricted access

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